Fig. REF  presents an overview of our proposed method. We use the recent multi-scale video transformer (MViT) architecture [1]} as the backbone network for video representation learning. We extend MViT by designing the Visual Token Embedding Module to generate the spatio-temporal tokens of both local visual patches and global visual context and feed them into the standard Multi-Head Self-Attention Module. We then utilize a novel Global-Local Correlation (GLC) Module to explicitly model the correlation between global and local visual tokens for gaze estimation. Finally, we make use of the Decoder Network to predict the gaze distribution based on the learned video representation from the GLC module.
